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cs.LG2026
Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables
Yasin Ibrahim, Hermione Warr, Robin J. Evans +1
Machine learning models can achieve strong test performance while relying on demographic or acquisition-related shortcuts. We propose counterfactual (CF) marginalisation as a test-…
cs.LG2026
Error Detection for PET/CT Radiology Reports: Domain-Specific vs Large Language Models
Hermione Warr, Harry Anthony, Lilli J Freischem +3
Errors in radiology reports can adversely affect patient treatment, yet automated report quality assurance remains challenging because errors are often subtle and require domain ex…
cs.LG2024
Semi-Supervised Learning for Deep Causal Generative Models
Yasin Ibrahim, Hermione Warr, Konstantinos Kamnitsas
Developing models that are capable of answering questions of the form "How would x change if y had been z?'" is fundamental to advancing medical image analysis. Training causal gen…